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Energy consumption in mobile phones: a measurement study and implications for network applications
TLDR
TailEnder is developed, a protocol that reduces energy consumption of common mobile applications and aggressively prefetches several times more data and improves user-specified response times while consuming less energy. Expand
Generating Coherent Event Schemas at Scale
TLDR
This work presents a novel approach to inducing open-domain event schemas that overcomes limitations of Chambers and Jurafsky's (2009) schemas and uses cooccurrence statistics of semantically typed relational triples, which it calls Rel-grams (relational n- grams). Expand
Exploring reductions for long web queries
TLDR
This paper proposes three learning formulations that combine effective and efficient query performance predictors to perform automatic query reduction and finds that the proposed techniques deliver consistent retrieval gains where it matters most: poorly performing long web queries. Expand
Event Representations with Tensor-based Compositions
TLDR
This work proposes a new tensor-based composition method for creating event representations that captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. Expand
What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
TLDR
This work develops an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges, and compares a retrieval and an inference solver on 212 questions. Expand
A study of the knowledge base requirements for passing an elementary science test
TLDR
The analysis suggests that as well as fact extraction from text and statistically driven rule extraction, three other styles of automatic knowledge base construction (AKBC) would be useful: acquiring definitional knowledge, direct 'reading' of rules from texts that state them, and, given a particular representational framework, acquisition of specific instances of those models from text. Expand
Markov Logic Networks for Natural Language Question Answering
TLDR
The experiments, demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task. Expand
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU
TLDR
MobiRNN is presented, a mobile-specific optimization framework that implements GPU offloading specifically for mobile GPUs that does significantly decrease the latency of running RNN models on phones. Expand
Exploring Markov Logic Networks for Question Answering
TLDR
A system that reasons with knowledge derived from textbooks, represented in a subset of firstorder logic, called Praline, which demonstrates a 15% accuracy boost and a 10x reduction in runtime as compared to other MLNbased methods, and comparable accuracy to word-based baseline approaches. Expand
Human Centered NLP with User-Factor Adaptation
TLDR
A continuous adaptation technique is introduced, suited for real-valued user factors that are common in social science and bringing us closer to personalized NLP, adapting to each user uniquely. Expand
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